[人工智能预测乳腺癌新辅助化疗的病理完整反应:当前的进展和挑战]
Sunwei He1, Xiujuan Li2, Yuanzhong Xie2
1Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.
概括
在乳腺癌中预测新辅助化疗 (NAC) 反应至关重要. 本综述探讨了用于预测病理完整反应 (pCR) 的统计学,机器学习和深度学习方法,强调了改善患者护理的未来方向.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 乳腺癌发病率不断上升,需要有效的手术前治疗,如新辅助化疗 (NAC).
- 预测NAC的病理完整反应 (pCR) 是非常重要的,因为患者的结果是可变的.
- 准确的PCR预测有助于量身定制治疗和改善患者的预后.
研究的目的:
- 综合审查和比较统计,机器学习 (ML) 和深度学习 (DL) 方法来预测NAC后的乳腺癌pCR.
- 检查这些预测方法的应用和局限性.
- 确定增强PCR预测模型的未来研究方向.
主要方法:
- 对乳腺癌pCR的预测模型现有文献的审查.
- 综合临床数据的统计方法的分析.
- 评估传统的ML和先进的DL方法用于特征提取和多式联络数据集成.
主要成果:
- 统计方法提供了使用临床数据的早期预测能力.
- 机器学习方法通过分析复杂的数据集来增强预测.
- 深度学习在自动特征提取和多模式数据集成中表现出卓越的性能,用于PCR预测.
结论:
- 深度学习对准确的乳腺癌pCR预测有显著的前景.
- 整合多样化的数据源和先进的AI模型是提高预测准确性的关键.
- 未来的工作重点应该是开发强大的临床整合模型,以优化患者护理和结果.
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